The PhD project aims to enhance the efficiency and selectivity of catalytic processes that convert synthesis gas (CO+H2 mixture, or syngas) into liquid hydrocarbons. As global energy demands increase, sustainable and efficient conversion technologies become paramount. This research focuses on designing and optimising structured catalytic surfaces that improve reaction kinetics and product yields in syngas-to-liquids (GTL) processes, which are expected to aid the near to medium term energy transtion. The synthesis gas can be obtained using natural gas, naphtha, biomass, municipal solid waste, petcoke, or coal; no matter which source, the final conversion of the gas to liquid fuels (specifically, methanol and ethanol) is the bottleneck for process scale-up. Utilizing principles of reaction engineering, multiphysics modeling and computational fluid dynamics (CFD), we will develop novel catalytic reactor architectures that promote optimal interaction between reactants and catalysts, to target optimal utilization of limiting reactants, or to maximize production of the liquid fuel. The project will examine a variety of structured surfaces, such as monoliths, and seek shape and structure optimization that can be achieved through 3D printing techniques. The optimization will be achieved through evolutionary techniques such as Non-Dominated Sorting Genetic Algorithm 2 (NSGA-II), or through data science techniques. The last part of the project will involve model validation using test reactions and measurement of temperature profiles and product yields on a limited number of optimal structure catalysts, which could be evolved through actually building the structured reactors through 3D printing and testing them under conditions designed to test the role of each of the transport effects. Background required: Bachelors or Masters degree in Chemical or Mechanical Engineering, or related areas. Strong interest and background in mathematical modeling is desirable.
“Elevating Horizons Through Discovery and Ingenuity”